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Migliorini F, Feierabend M, Hofmann UK. Fostering Excellence in Knee Arthroplasty: Developing Optimal Patient Care Pathways and Inspiring Knowledge Transfer of Advanced Surgical Techniques. J Healthc Leadersh 2023; 15:327-338. [PMID: 38020721 PMCID: PMC10676205 DOI: 10.2147/jhl.s383916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/13/2023] [Indexed: 12/01/2023] Open
Abstract
Osteoarthritis of the knee is common. Early sports trauma or cartilage defects are risk factors for osteoarthritis. If conservative treatment fails, partial or total joint replacement is often performed. A joint replacement aims to restore physiological biomechanics and the quality of life of affected patients. Total knee arthroplasty is one of the most performed surgeries in musculoskeletal medicine. Several developments have taken place over the last decades that have truly altered the way we look at knee arthroplasty today. Some of the fascinating aspects will be presented and discussed in the present narrative review.
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Affiliation(s)
- Filippo Migliorini
- Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University Medical Centre, Aachen, 52074, Germany
- Department of Orthopedics and Trauma Surgery, Academic Hospital of Bolzano (SABES-ASDAA), Teaching Hospital of Paracelsus Medical University, 39100 Bolzano, Italy
| | - Martina Feierabend
- Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University Medical Centre, Aachen, 52074, Germany
| | - Ulf Krister Hofmann
- Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University Medical Centre, Aachen, 52074, Germany
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2
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Kim SE, Han HS. Robotic-assisted unicompartmental knee arthroplasty: historical perspectives and current innovations. Biomed Eng Lett 2023; 13:543-552. [PMID: 37872988 PMCID: PMC10590358 DOI: 10.1007/s13534-023-00323-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/14/2023] [Accepted: 09/16/2023] [Indexed: 10/25/2023] Open
Abstract
Robotic assisted unicompartmental knee arthroplasty (RAUKA) has emerged as a successful approach for optimizing implant positioning accuracy, minimizing soft tissue injury, and improving patient-reported outcomes. The application of RAUKA is expected to increase because of its advantages over conventional unicompartmental knee arthroplasty. This review article provides an overview of RAUKA, encompassing the historical development of the procedure, the features of the robotic arm and navigation systems, and the characteristics of contemporary RAUKA. The article also includes a comparison between conventional unicompartmental arthroplasty and RAUKA, as well as a discussion of current challenges and future advancements in the field of RAUKA.
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Affiliation(s)
- Sung Eun Kim
- Department of Orthopaedic Surgery, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744 Republic of Korea
| | - Hyuk-Soo Han
- Department of Orthopaedic Surgery, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 110-744 Republic of Korea
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Mavrogenis AF, Scarlat MM. Thoughts on artificial intelligence use in medical practice and in scientific writing. INTERNATIONAL ORTHOPAEDICS 2023; 47:2139-2141. [PMID: 37581692 DOI: 10.1007/s00264-023-05936-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Affiliation(s)
- Andreas F Mavrogenis
- First Department of Orthopaedics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
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Lewandrowski KU, Elfar JC, Li ZM, Burkhardt BW, Lorio MP, Winkler PA, Oertel JM, Telfeian AE, Dowling Á, Vargas RAA, Ramina R, Abraham I, Assefi M, Yang H, Zhang X, Ramírez León JF, Fiorelli RKA, Pereira MG, de Carvalho PST, Defino H, Moyano J, Lim KT, Kim HS, Montemurro N, Yeung A, Novellino P. The Changing Environment in Postgraduate Education in Orthopedic Surgery and Neurosurgery and Its Impact on Technology-Driven Targeted Interventional and Surgical Pain Management: Perspectives from Europe, Latin America, Asia, and The United States. J Pers Med 2023; 13:852. [PMID: 37241022 PMCID: PMC10221956 DOI: 10.3390/jpm13050852] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023] Open
Abstract
Personalized care models are dominating modern medicine. These models are rooted in teaching future physicians the skill set to keep up with innovation. In orthopedic surgery and neurosurgery, education is increasingly influenced by augmented reality, simulation, navigation, robotics, and in some cases, artificial intelligence. The postpandemic learning environment has also changed, emphasizing online learning and skill- and competency-based teaching models incorporating clinical and bench-top research. Attempts to improve work-life balance and minimize physician burnout have led to work-hour restrictions in postgraduate training programs. These restrictions have made it particularly challenging for orthopedic and neurosurgery residents to acquire the knowledge and skill set to meet the requirements for certification. The fast-paced flow of information and the rapid implementation of innovation require higher efficiencies in the modern postgraduate training environment. However, what is taught typically lags several years behind. Examples include minimally invasive tissue-sparing techniques through tubular small-bladed retractor systems, robotic and navigation, endoscopic, patient-specific implants made possible by advances in imaging technology and 3D printing, and regenerative strategies. Currently, the traditional roles of mentee and mentor are being redefined. The future orthopedic surgeons and neurosurgeons involved in personalized surgical pain management will need to be versed in several disciplines ranging from bioengineering, basic research, computer, social and health sciences, clinical study, trial design, public health policy development, and economic accountability. Solutions to the fast-paced innovation cycle in orthopedic surgery and neurosurgery include adaptive learning skills to seize opportunities for innovation with execution and implementation by facilitating translational research and clinical program development across traditional boundaries between clinical and nonclinical specialties. Preparing the future generation of surgeons to have the aptitude to keep up with the rapid technological advances is challenging for postgraduate residency programs and accreditation agencies. However, implementing clinical protocol change when the entrepreneur-investigator surgeon substantiates it with high-grade clinical evidence is at the heart of personalized surgical pain management.
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Affiliation(s)
- Kai-Uwe Lewandrowski
- Center For Advanced Spine Care of Southern Arizona, 4787 E Camp Lowell Drive, Tucson, AZ 85719, USA
- Department of Orthopaedics, Fundación Universitaria Sanitas, Bogotá 111321, Colombia
| | - John C. Elfar
- Department of Orthopaedic Surgery, College of Medicine—Tucson Campus, Health Sciences Innovation Building (HSIB), University of Arizona, 1501 N. Campbell Avenue, Tower 4, 8th Floor, Suite 8401, Tucson, AZ 85721, USA;
| | - Zong-Ming Li
- Departments of Orthopaedic Surgery and Biomedical Engineering, College of Medicine—Tucson Campus, Health Sciences Innovation Building (HSIB), University of Arizona, 1501 N. Campbell Avenue, Tower 4, 8th Floor, Suite 8401, Tucson, AZ 85721, USA;
| | - Benedikt W. Burkhardt
- Wirbelsäulenzentrum/Spine Center—WSC, Hirslanden Klinik Zurich, Witellikerstrasse 40, 8032 Zurich, Switzerland;
| | - Morgan P. Lorio
- Advanced Orthopaedics, 499 E. Central Pkwy, Ste. 130, Altamonte Springs, FL 32701, USA;
| | - Peter A. Winkler
- Department of Neurosurgery, Charite Universitaetsmedizin Berlin, 13353 Berlin, Germany;
| | - Joachim M. Oertel
- Klinik für Neurochirurgie, Universitätsdes Saarlandes, Kirrberger Straße 100, 66421 Homburg, Germany;
| | - Albert E. Telfeian
- Department of Neurosurgery, Rhode Island Hospital, The Warren Alpert Medical School of Brown University, Providence, RI 02903, USA;
| | - Álvaro Dowling
- Orthopaedic Surgery, University of São Paulo, Brazilian Spine Society (SBC), Ribeirão Preto 14071-550, Brazil; (Á.D.); (H.D.)
| | - Roth A. A. Vargas
- Department of Neurosurgery, Foundation Hospital Centro Médico Campinas, Campinas 13083-210, Brazil;
| | - Ricardo Ramina
- Neurological Institute of Curitiba, Curitiba 80230-030, Brazil;
| | - Ivo Abraham
- Clinical Translational Sciences, University of Arizona, Roy P. Drachman Hall, Rm. B306H, Tucson, AZ 85721, USA;
| | - Marjan Assefi
- Department of Biology, Nano-Biology, University of North Carolina, Greensboro, NC 27413, USA;
| | - Huilin Yang
- Orthopaedic Department, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou 215031, China;
| | - Xifeng Zhang
- Department of Orthopaedics, First Medical Center, PLA General Hospital, Beijing 100853, China;
| | - Jorge Felipe Ramírez León
- Minimally Invasive Spine Center Bogotá D.C. Colombia, Reina Sofía Clinic Bogotá D.C. Colombia, Department of Orthopaedics Fundación Universitaria Sanitas, Bogotá 0819, Colombia;
| | - Rossano Kepler Alvim Fiorelli
- Department of General and Specialized Surgery, Gaffrée e Guinle University Hospital, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro 20270-004, Brazil;
| | - Mauricio G. Pereira
- Faculty of Medecine, University of Brasilia, Federal District, Brasilia 70919-900, Brazil;
| | | | - Helton Defino
- Orthopaedic Surgery, University of São Paulo, Brazilian Spine Society (SBC), Ribeirão Preto 14071-550, Brazil; (Á.D.); (H.D.)
| | - Jaime Moyano
- La Sociedad Iberolatinoamericana De Columna (SILACO), and the Spine Committee of the Ecuadorian Society of Orthopaedics and Traumatology (Comité de Columna de la Sociedad Ecuatoriana de Ortopedia y Traumatología), Quito 170521, Ecuador;
| | - Kang Taek Lim
- Good Doctor Teun Teun Spine Hospital, Anyang 14041, Republic of Korea;
| | - Hyeun-Sung Kim
- Department of Neurosurgery, Nanoori Hospital, Seoul 06048, Republic of Korea;
| | - Nicola Montemurro
- Department of Neurosurgery, Azienda Ospedaliero Universitaria Pisana, University of Pisa, 56124 Pisa, Italy;
| | - Anthony Yeung
- Desert Institute for Spine Care, Phoenix, AZ 85020, USA;
| | - Pietro Novellino
- Guinle and State Institute of Diabetes and Endocrinology, Rio de Janeiro 20270-004, Brazil;
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Mavrogenis AF, Scarlat MM. Artificial intelligence publications: synthetic data, patients, and papers. INTERNATIONAL ORTHOPAEDICS 2023; 47:1395-1396. [PMID: 37162553 DOI: 10.1007/s00264-023-05830-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Affiliation(s)
- Andreas F Mavrogenis
- First Department of Orthopaedics, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece
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Abbas J, Yousef M, Peled N, Hershkovitz I, Hamoud K. Predictive factors for degenerative lumbar spinal stenosis: a model obtained from a machine learning algorithm technique. BMC Musculoskelet Disord 2023; 24:218. [PMID: 36949452 PMCID: PMC10035245 DOI: 10.1186/s12891-023-06330-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 03/16/2023] [Indexed: 03/24/2023] Open
Abstract
BACKGROUND Degenerative lumbar spinal stenosis (DLSS) is the most common spine disease in the elderly population. It is usually associated with lumbar spine joints/or ligaments degeneration. Machine learning technique is an exclusive method for handling big data analysis; however, the development of this method for spine pathology is rare. This study aims to detect the essential variables that predict the development of symptomatic DLSS using the random forest of machine learning (ML) algorithms technique. METHODS A retrospective study with two groups of individuals. The first included 165 with symptomatic DLSS (sex ratio 80 M/85F), and the second included 180 individuals from the general population (sex ratio: 90 M/90F) without lumbar spinal stenosis symptoms. Lumbar spine measurements such as vertebral or spinal canal diameters from L1 to S1 were conducted on computerized tomography (CT) images. Demographic and health data of all the participants (e.g., body mass index and diabetes mellitus) were also recorded. RESULTS The decision tree model of ML demonstrate that the anteroposterior diameter of the bony canal at L5 (males) and L4 (females) levels have the greatest stimulus for symptomatic DLSS (scores of 1 and 0.938). In addition, combination of these variables with other lumbar spine features is mandatory for developing the DLSS. CONCLUSIONS Our results indicate that combination of lumbar spine characteristics such as bony canal and vertebral body dimensions rather than the presence of a sole variable is highly associated with symptomatic DLSS onset.
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Affiliation(s)
- Janan Abbas
- Department of Physical Therapy, Zefat Academic College, 13206, Zefat, Israel.
- Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel.
| | - Malik Yousef
- Department of Information Systems, Zefat Academic College, Zefat, Israel
| | - Natan Peled
- Department of Radiology, Carmel Medical Center, 3436212, Haifa, Israel
| | - Israel Hershkovitz
- Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, 6997801, Tel Aviv, Israel
| | - Kamal Hamoud
- Department of Physical Therapy, Zefat Academic College, 13206, Zefat, Israel
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Patient Perspectives on Artificial Intelligence in Healthcare Decision Making: A Multi-Center Comparative Study. Indian J Orthop 2023; 57:653-665. [PMID: 37122674 PMCID: PMC9979110 DOI: 10.1007/s43465-023-00845-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 02/10/2023] [Indexed: 03/06/2023]
Abstract
Objective Investigate the patient opinion on the use of Artificial Intelligence (AI) in Orthopaedics. Methods 397 orthopaedic patients from a large urban academic center and a rural health system completed a 37-component survey querying patient demographics and perspectives on clinical scenarios involving AI. An average comfort score was calculated from thirteen Likert-scale questions (1, not comfortable; 10, very comfortable). Secondary outcomes requested a binary opinion on whether it is acceptable for patient healthcare data to be used to create AI (yes/no) and the impact of AI on: orthopaedic care (positive/negative); healthcare cost (increase/decrease); and their decision to refuse healthcare if cost increased (yes/no). Bivariate and multivariable analyses were employed to identify characteristics that impacted patient perspectives. Results The average comfort score across the population was 6.4, with significant bivariate differences between age (p = 0.0086), gender (p = 0.0001), education (p = 0.0029), experience with AI/ML (p < 0.0001), survey format (p < 0.0001), and four binary outcomes (p < 0.05). When controlling for age and education, multivariable regression identified significant relationships between comfort score and experience with AI/ML (p = 0.0018) and each of the four binary outcomes (p < 0.05). In the final multivariable model gender, survey format, perceived impact of AI on orthopaedic care, and the decision to refuse care if it were to increase cost remained significantly associated with the average AI comfort score (p < 0.05). Additionally, patients were not comfortable undergoing surgery entirely by a robot with distant physician supervision compared to close supervision. Conclusion The orthopaedic patient appears comfortable with AI joining the care team.
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Orthopedic surgeons’ attitudes and expectations toward artificial intelligence: A national survey study. JOURNAL OF SURGERY AND MEDICINE 2023. [DOI: 10.28982/josam.7709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023] Open
Abstract
Background/Aim: There is a lack of understanding of artificial intelligence (AI) among orthopedic surgeons regarding how it can be used in their clinical practices. This study aimed to evaluate the attitudes of orthopedic surgeons regarding the application of AI in their practices.
Methods: A cross-sectional study was conducted in Turkey among 189 orthopedic surgeons between November 2021 and February 2022. An electronic survey was designed using the SurveyMonkey platform. The questionnaire included six subsections related to AI usefulness in clinical practice and participants’ knowledge about the topic. It also surveyed their acceptance level of learning, concerns about the potential risks of AI, and implementation of this technology into their daily practice
Results: A total of 33.9% of the participants indicated that they were familiar with the concept of AI, while 82.5% planned to learn about artificial intelligence in the coming years. Most of the surgeons (68.3%) reported not using AI in their daily practice. The activities of orthopedic associations focused on AI were insufficient according to 77.2% of participants. Orthopedic surgeons expressed concern over AI involvement in the future regarding an insensitive and nonempathic attitude toward the patient (53.5%). A majority of respondents (80.4%) indicated that AI was most feasible in extremity reconstruction. Pelvis fractures were found in the region where the AI system is most needed in the fracture classification (68.7%).
Conclusion: Most of the respondents did not use AI in their daily clinical practice; however, almost all surgeons had plans to learn about artificial intelligence in the future. There was a need to improve orthopedic associations’ activities focusing on artificial intelligence. Furthermore, new research including the medical ethics issues of the field will be needed to allay the surgeons’ worries. The classification system of pelvic fractures and sub-branches of orthopedic extremity reconstruction were the most feasible areas for AI systems. We believe that this study will serve as a guide for all branches of orthopedic medicine.
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Benzakour A, Altsitzioglou P, Lemée JM, Ahmad A, Mavrogenis AF, Benzakour T. Artificial intelligence in spine surgery. INTERNATIONAL ORTHOPAEDICS 2023; 47:457-465. [PMID: 35902390 DOI: 10.1007/s00264-022-05517-8] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 07/11/2022] [Indexed: 01/28/2023]
Abstract
The continuous progress of research and clinical trials has offered a wide variety of information concerning the spine and the treatment of the different spinal pathologies that may occur. Planning the best therapy for each patient could be a very difficult and challenging task as it often requires thorough processing of the patient's history and individual characteristics by the clinician. Clinicians and researchers also face problems when it comes to data availability due to patients' personal information protection policies. Artificial intelligence refers to the reproduction of human intelligence via special programs and computers that are trained in a way that simulates human cognitive functions. Artificial intelligence implementations to daily clinical practice such as surgical robots that facilitate spine surgery and reduce radiation dosage to medical staff, special algorithms that can predict the possible outcomes of conservative versus surgical treatment in patients with low back pain and disk herniations, and systems that create artificial populations with great resemblance and similar characteristics to real patients are considered to be a novel breakthrough in modern medicine. To enhance the body of the related literature and inform the readers on the clinical applications of artificial intelligence, we performed this review to discuss the contribution of artificial intelligence in spine surgery and pathology.
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Affiliation(s)
- Ahmed Benzakour
- Centre Orléanais du Dos - Pôle Santé Oréliance, Saran, France
| | - Pavlos Altsitzioglou
- First Department of Orthopaedics, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece
| | - Jean Michel Lemée
- Department of Neurosurgery, University Hospital of Angers, Angers, France
| | | | - Andreas F Mavrogenis
- First Department of Orthopaedics, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece.
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Scala A, Borrelli A, Improta G. Predictive analysis of lower limb fractures in the orthopedic complex operative unit using artificial intelligence: the case study of AOU Ruggi. Sci Rep 2022; 12:22153. [PMID: 36550192 PMCID: PMC9780352 DOI: 10.1038/s41598-022-26667-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
The length of stay (LOS) in hospital is one of the main parameters for evaluating the management of a health facility, of its departments in relation to the different specializations. Healthcare costs are in fact closely linked to this parameter as well as the profit margin. In the orthopedic field, the provision of this parameter is increasingly complex and of fundamental importance in order to be able to evaluate the planning of resources, the waiting times for any scheduled interventions and the management of the department and related surgical interventions. The purpose of this work is to predict and evaluate the LOS value using machine learning methods and applying multiple linear regression, starting from clinical data of patients hospitalized with lower limb fractures. The data were collected at the "San Giovanni di Dio e Ruggi d'Aragona" hospital in Salerno (Italy).
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Affiliation(s)
- Arianna Scala
- grid.4691.a0000 0001 0790 385XDepartment of Public Health, University of Naples “Federico II”, Naples, Italy
| | - Anna Borrelli
- San Giovanni di Dio e Ruggi d’Aragona” University Hospital, Salerno, Italy
| | - Giovanni Improta
- grid.4691.a0000 0001 0790 385XDepartment of Public Health, University of Naples “Federico II”, Naples, Italy ,Interdepartmental Center for Research in Healthcare Management and Innovation in Healthcare (CIRMIS), Naples, Italy
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Kumar V, Patel S, Baburaj V, Vardhan A, Singh PK, Vaishya R. Current understanding on artificial intelligence and machine learning in orthopaedics - A scoping review. J Orthop 2022; 34:201-206. [PMID: 36104993 PMCID: PMC9465367 DOI: 10.1016/j.jor.2022.08.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 11/25/2022] Open
Abstract
Background Artificial Intelligence (AI) has improved the way of looking at technological challenges. Today, we can afford to see many of the problems as just an input-output system rather than solving from the first principles. The field of Orthopaedics is not spared from this rapidly expanding technology. The recent surge in the use of AI can be attributed mainly to advancements in deep learning methodologies and computing resources. This review was conducted to draw an outline on the role of AI in orthopaedics. Methods We developed a search strategy and looked for articles on PubMed, Scopus, and EMBASE. A total of 40 articles were selected for this study, from tools for medical aid like imaging solutions, implant management, and robotic surgery to understanding scientific questions. Results A total of 40 studies have been included in this review. The role of AI in the various subspecialties such as arthroplasty, trauma, orthopaedic oncology, foot and ankle etc. have been discussed in detail. Conclusion AI has touched most of the aspects of Orthopaedics. The increase in technological literacy, data management plans, and hardware systems, amalgamated with the access to hand-held devices like mobiles, and electronic pads, augur well for the exciting times ahead in this field. We have discussed various technological breakthroughs in AI that have been able to perform in Orthopaedics, and also the limitations and the problem with the black-box approach of modern AI algorithms. We advocate for better interpretable algorithms which can help both the patients and surgeons alike.
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Affiliation(s)
- Vishal Kumar
- Department of Orthopaedics, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Sandeep Patel
- Department of Orthopaedics, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Vishnu Baburaj
- Department of Orthopaedics, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Aditya Vardhan
- Department of Orthopaedics, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Prasoon Kumar Singh
- Department of Orthopaedics, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
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Ren G, Yu K, Xie Z, Wang P, Zhang W, Huang Y, Wang Y, Wu X. Current Applications of Machine Learning in Spine: From Clinical View. Global Spine J 2022; 12:1827-1840. [PMID: 34628966 PMCID: PMC9609532 DOI: 10.1177/21925682211035363] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES This review aims to present current applications of machine learning (ML) in spine domain to clinicians. METHODS We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. RESULTS Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. CONCLUSIONS ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.
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Affiliation(s)
- GuanRui Ren
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Kun Yu
- Nanjing Jiangbei Hospital, Nanjing,
Jiangsu, China
| | - ZhiYang Xie
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - PeiYang Wang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Wei Zhang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Yong Huang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - YunTao Wang
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,YunTao Wang, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
| | - XiaoTao Wu
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,XiaoTao Wu, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
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Batailler C, Shatrov J, Sappey-Marinier E, Servien E, Parratte S, Lustig S. Artificial intelligence in knee arthroplasty: current concept of the available clinical applications. ARTHROPLASTY 2022; 4:17. [PMID: 35491420 PMCID: PMC9059406 DOI: 10.1186/s42836-022-00119-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Accepted: 02/24/2022] [Indexed: 11/30/2022] Open
Abstract
Background Artificial intelligence (AI) is defined as the study of algorithms that allow machines to reason and perform cognitive functions such as problem-solving, objects, images, word recognition, and decision-making. This study aimed to review the published articles and the comprehensive clinical relevance of AI-based tools used before, during, and after knee arthroplasty. Methods The search was conducted through PubMed, EMBASE, and MEDLINE databases from 2000 to 2021 using the 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA). Results A total of 731 potential articles were reviewed, and 132 were included based on the inclusion criteria and exclusion criteria. Some steps of the knee arthroplasty procedure were assisted and improved by using AI-based tools. Before surgery, machine learning was used to aid surgeons in optimizing decision-making. During surgery, the robotic-assisted systems improved the accuracy of knee alignment, implant positioning, and ligamentous balance. After surgery, remote patient monitoring platforms helped to capture patients’ functional data. Conclusion In knee arthroplasty, the AI-based tools improve the decision-making process, surgical planning, accuracy, and repeatability of surgical procedures.
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St Mart JP, Goh EL, Liew I, Shah Z, Sinha J. Artificial intelligence in orthopaedic surgery: transforming technological innovation in patient care and surgical training. Postgrad Med J 2022:postgradmedj-2022-141596. [PMID: 35379754 DOI: 10.1136/postgradmedj-2022-141596] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 03/19/2022] [Indexed: 12/16/2022]
Abstract
Artificial intelligence (AI) is an exciting field combining computer science with robust data sets to facilitate problem-solving. It has the potential to transform education, practice and delivery of healthcare especially in orthopaedics. This review article outlines some of the already used AI pathways as well as recent technological advances in orthopaedics. Additionally, this article further explains how potentially these two entities could be combined in the future to improve surgical education, training and ultimately patient care and outcomes.
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Affiliation(s)
- Jean-Pierre St Mart
- Trauma and Orthopaedics, North West Anglia NHS Foundation Trust, Peterborough, UK
| | - En Lin Goh
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Oxford Trauma, Kadoorie Centre, University of Oxford, Oxford, UK
| | - Ignatius Liew
- Trauma and Orthopaedics, North West Anglia NHS Foundation Trust, Peterborough, UK
| | - Zameer Shah
- Trauma and Orthopaedic Surgery, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Joydeep Sinha
- Trauma and Orthopaedic Surgery, King's College Hospital NHS Foundation Trust, London, UK
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15
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Invited Commentary on: Artificial Neural Networks Predict 30-Day Mortality After Hip Fracture: Insights From Machine Learning. J Am Acad Orthop Surg 2022; 30:e506-e507. [PMID: 35143463 DOI: 10.5435/jaaos-d-20-01142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 11/03/2020] [Indexed: 02/01/2023] Open
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16
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SHIFTing artificial intelligence to be responsible in healthcare: A systematic review. Soc Sci Med 2022; 296:114782. [DOI: 10.1016/j.socscimed.2022.114782] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 02/02/2022] [Accepted: 02/03/2022] [Indexed: 12/12/2022]
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17
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Li MD, Ahmed SR, Choy E, Lozano-Calderon SA, Kalpathy-Cramer J, Chang CY. Artificial intelligence applied to musculoskeletal oncology: a systematic review. Skeletal Radiol 2022; 51:245-256. [PMID: 34013447 DOI: 10.1007/s00256-021-03820-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/13/2021] [Accepted: 05/13/2021] [Indexed: 02/02/2023]
Abstract
Developments in artificial intelligence have the potential to improve the care of patients with musculoskeletal tumors. We performed a systematic review of the published scientific literature to identify the current state of the art of artificial intelligence applied to musculoskeletal oncology, including both primary and metastatic tumors, and across the radiology, nuclear medicine, pathology, clinical research, and molecular biology literature. Through this search, we identified 252 primary research articles, of which 58 used deep learning and 194 used other machine learning techniques. Articles involving deep learning have mostly involved bone scintigraphy, histopathology, and radiologic imaging. Articles involving other machine learning techniques have mostly involved transcriptomic analyses, radiomics, and clinical outcome prediction models using medical records. These articles predominantly present proof-of-concept work, other than the automated bone scan index for bone metastasis quantification, which has translated to clinical workflows in some regions. We systematically review and discuss this literature, highlight opportunities for multidisciplinary collaboration, and identify potentially clinically useful topics with a relative paucity of research attention. Musculoskeletal oncology is an inherently multidisciplinary field, and future research will need to integrate and synthesize noisy siloed data from across clinical, imaging, and molecular datasets. Building the data infrastructure for collaboration will help to accelerate progress towards making artificial intelligence truly useful in musculoskeletal oncology.
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Affiliation(s)
- Matthew D Li
- Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. .,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Syed Rakin Ahmed
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Harvard Medical School, Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA.,Geisel School of Medicine At Dartmouth, Dartmouth College, Hanover, NH, USA
| | - Edwin Choy
- Division of Hematology Oncology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Santiago A Lozano-Calderon
- Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Connie Y Chang
- Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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18
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Federer SJ, Jones GG. Artificial intelligence in orthopaedics: A scoping review. PLoS One 2021; 16:e0260471. [PMID: 34813611 PMCID: PMC8610245 DOI: 10.1371/journal.pone.0260471] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 11/11/2021] [Indexed: 11/19/2022] Open
Abstract
There is a growing interest in the application of artificial intelligence (AI) to orthopaedic surgery. This review aims to identify and characterise research in this field, in order to understand the extent, range and nature of this work, and act as springboard to stimulate future studies. A scoping review, a form of structured evidence synthesis, was conducted to summarise the use of AI in orthopaedics. A literature search (1946-2019) identified 222 studies eligible for inclusion. These studies were predominantly small and retrospective. There has been significant growth in the number of papers published in the last three years, mainly from the USA (37%). The majority of research used AI for image interpretation (45%) or as a clinical decision tool (25%). Spine (43%), knee (23%) and hip (14%) were the regions of the body most commonly studied. The application of artificial intelligence to orthopaedics is growing. However, the scope of its use so far remains limited, both in terms of its possible clinical applications, and the sub-specialty areas of the body which have been studied. A standardized method of reporting AI studies would allow direct assessment and comparison. Prospective studies are required to validate AI tools for clinical use.
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Affiliation(s)
- Simon J. Federer
- MSk Lab, Sir Michael Uren Hub, Imperial College London, London, United Kingdom
- * E-mail:
| | - Gareth G. Jones
- MSk Lab, Sir Michael Uren Hub, Imperial College London, London, United Kingdom
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19
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Litvin A, Korenev S, Rumovskaya S, Sartelli M, Baiocchi G, Biffl WL, Coccolini F, Di Saverio S, Kelly MD, Kluger Y, Leppäniemi A, Sugrue M, Catena F. WSES project on decision support systems based on artificial neural networks in emergency surgery. World J Emerg Surg 2021; 16:50. [PMID: 34565420 PMCID: PMC8474926 DOI: 10.1186/s13017-021-00394-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 09/13/2021] [Indexed: 12/11/2022] Open
Abstract
The article is a scoping review of the literature on the use of decision support systems based on artificial neural networks in emergency surgery. The authors present modern literature data on the effectiveness of artificial neural networks for predicting, diagnosing and treating abdominal emergency conditions: acute appendicitis, acute pancreatitis, acute cholecystitis, perforated gastric or duodenal ulcer, acute intestinal obstruction, and strangulated hernia. The intelligent systems developed at present allow a surgeon in an emergency setting, not only to check his own diagnostic and prognostic assumptions, but also to use artificial intelligence in complex urgent clinical cases. The authors summarize the main limitations for the implementation of artificial neural networks in surgery and medicine in general. These limitations are the lack of transparency in the decision-making process; insufficient quality educational medical data; lack of qualified personnel; high cost of projects; and the complexity of secure storage of medical information data. The development and implementation of decision support systems based on artificial neural networks is a promising direction for improving the forecasting, diagnosis and treatment of emergency surgical diseases and their complications.
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Affiliation(s)
- Andrey Litvin
- Department of Surgical Disciplines, Immanuel Kant Baltic Federal University, Kaliningrad, Russia.
| | - Sergey Korenev
- Department of Surgical Disciplines, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Sophiya Rumovskaya
- Kaliningrad Branch of Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Kaliningrad, Russia
| | | | - Gianluca Baiocchi
- Surgical Clinic, Department of Experimental and Clinical Sciences, University of Brescia, Brescia, Italy
| | - Walter L Biffl
- Division of Trauma and Acute Care Surgery, Scripps Memorial Hospital La Jolla, La Jolla, CA, USA
| | - Federico Coccolini
- General, Emergency and Trauma Surgery Department, Pisa University Hospital, Pisa, Italy
| | - Salomone Di Saverio
- Department of Surgery, Cambridge University Hospital, NHS Foundation Trust, Cambridge, UK
| | | | - Yoram Kluger
- Department of General Surgery, Rambam Healthcare Campus, Haifa, Israel
| | - Ari Leppäniemi
- Department of Gastrointestinal Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Michael Sugrue
- Donegal Clinical Research Academy, Letterkenny University Hospital, Donegal, Ireland
| | - Fausto Catena
- Department of Emergency and Trauma Surgery of the University Hospital of Parma, Parma, Italy
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20
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Artificial Intelligence and the Future of Spine Surgery: A Practical Supplement to Modern Spine Care? Clin Spine Surg 2021; 34:216-219. [PMID: 33290325 DOI: 10.1097/bsd.0000000000001119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Accepted: 11/07/2020] [Indexed: 10/22/2022]
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21
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Abstract
Artificial intelligence is an exciting and growing field in medicine to assist in the proper diagnosis of patients. Although the use of artificial intelligence in orthopedics is currently limited, its utility in other fields has been extremely valuable and could be useful in orthopedics, especially spine care. Automated systems have the ability to analyze complex patterns and images, which will allow for enhanced analysis of imaging. Although the potential impact of artificial intelligence integration into spine care is promising, there are several limitations that must be overcome. Our goal is to review current advances that machine learning has been used for in orthopedics, and discuss potential application to spine care in the clinical setting in which there is a need for the development of automated systems.
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22
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Augmented Reality, Virtual Reality and Artificial Intelligence in Orthopedic Surgery: A Systematic Review. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11073253] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background: The application of virtual and augmented reality technologies to orthopaedic surgery training and practice aims to increase the safety and accuracy of procedures and reducing complications and costs. The purpose of this systematic review is to summarise the present literature on this topic while providing a detailed analysis of current flaws and benefits. Methods: A comprehensive search on the PubMed, Cochrane, CINAHL, and Embase database was conducted from inception to February 2021. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were used to improve the reporting of the review. The Cochrane Risk of Bias Tool and the Methodological Index for Non-Randomized Studies (MINORS) was used to assess the quality and potential bias of the included randomized and non-randomized control trials, respectively. Results: Virtual reality has been proven revolutionary for both resident training and preoperative planning. Thanks to augmented reality, orthopaedic surgeons could carry out procedures faster and more accurately, improving overall safety. Artificial intelligence (AI) is a promising technology with limitless potential, but, nowadays, its use in orthopaedic surgery is limited to preoperative diagnosis. Conclusions: Extended reality technologies have the potential to reform orthopaedic training and practice, providing an opportunity for unidirectional growth towards a patient-centred approach.
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23
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Magan AA, Kayani B, Chang JS, Roussot M, Moriarty P, Haddad FS. Artificial intelligence and surgical innovation: lower limb arthroplasty. Br J Hosp Med (Lond) 2020; 81:1-7. [PMID: 33135934 DOI: 10.12968/hmed.2020.0309] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The number of patients requiring hip and knee arthroplasty continues to rise each year. Patients are living longer and expecting to remain active into later life following joint replacement. Developments in computer-assisted surgery and robotic technology may optimise surgical outcomes and patient satisfaction following lower limb arthroplasty. The use of artificial intelligence in healthcare is rapidly growing and has gained momentum in lower limb arthroplasty. This article reviews the use of artificial intelligence and surgical innovation in lower limb arthroplasty, with a particular focus on robotic-assisted surgery in total knee arthroplasty.
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Affiliation(s)
- A A Magan
- Department of Trauma and Orthopaedics, University College London Hospital NHS Foundation Trust, London, UK.,Department of Orthopaedics, The Princess Grace Hospital, London, UK
| | - B Kayani
- Department of Trauma and Orthopaedics, University College London Hospital NHS Foundation Trust, London, UK
| | - J S Chang
- Department of Trauma and Orthopaedics, University College London Hospital NHS Foundation Trust, London, UK
| | - M Roussot
- Department of Trauma and Orthopaedics, University College London Hospital NHS Foundation Trust, London, UK
| | - P Moriarty
- Department of Trauma and Orthopaedics, University College London Hospital NHS Foundation Trust, London, UK
| | - F S Haddad
- Department of Trauma and Orthopaedics, University College London Hospital NHS Foundation Trust, London, UK.,Department of Trauma and Orthopaedics, The Princess Grace Hospital, London, UK
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24
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Chang M, Canseco JA, Nicholson KJ, Patel N, Vaccaro AR. The Role of Machine Learning in Spine Surgery: The Future Is Now. Front Surg 2020; 7:54. [PMID: 32974382 PMCID: PMC7472375 DOI: 10.3389/fsurg.2020.00054] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 07/13/2020] [Indexed: 12/12/2022] Open
Abstract
The recent influx of machine learning centered investigations in the spine surgery literature has led to increased enthusiasm as to the prospect of using artificial intelligence to create clinical decision support tools, optimize postoperative outcomes, and improve technologies used in the operating room. However, the methodology underlying machine learning in spine research is often overlooked as the subject matter is quite novel and may be foreign to practicing spine surgeons. Improper application of machine learning is a significant bioethics challenge, given the potential consequences of over- or underestimating the results of such studies for clinical decision-making processes. Proper peer review of these publications requires a baseline familiarity of the language associated with machine learning, and how it differs from classical statistical analyses. This narrative review first introduces the overall field of machine learning and its role in artificial intelligence, and defines basic terminology. In addition, common modalities for applying machine learning, including classification and regression decision trees, support vector machines, and artificial neural networks are examined in the context of examples gathered from the spine literature. Lastly, the ethical challenges associated with adapting machine learning for research related to patient care, as well as future perspectives on the potential use of machine learning in spine surgery, are discussed specifically.
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Affiliation(s)
- Michael Chang
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States
- Rothman Orthopaedic Institute, Philadelphia, PA, United States
| | - Jose A. Canseco
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States
- Rothman Orthopaedic Institute, Philadelphia, PA, United States
| | | | - Neil Patel
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States
- Rothman Orthopaedic Institute, Philadelphia, PA, United States
| | - Alexander R. Vaccaro
- Department of Orthopaedic Surgery, Thomas Jefferson University, Philadelphia, PA, United States
- Rothman Orthopaedic Institute, Philadelphia, PA, United States
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25
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Artificial intelligence in orthopedic surgery: current state and future perspective. Chin Med J (Engl) 2020; 132:2521-2523. [PMID: 31658155 PMCID: PMC6846263 DOI: 10.1097/cm9.0000000000000479] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
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26
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Translational medicine: Challenges and new orthopaedic vision (Mediouni-Model). CURRENT ORTHOPAEDIC PRACTICE 2020. [DOI: 10.1097/bco.0000000000000846] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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